Performance trials on different rates and ratios of N and P fertilisation in Ethiopia to inform field-specific Maize-Nutrient-Management advisory

This report of the Scaling Readiness of Nutrient Management decision Support Tools project focuses on agronomic trials that serve to inform the development of scalable, field-specific advisory for maize farmers in Ethiopia. These trials were conducted to generate additional information required to make a mobile phone-based nutrient decision support tool – Maize-Nutrient-Manager – more scalable in the context of institutional limitations in fertilizer availability and distribution in Ethiopia. The focus of the trials is on establishing proper N:P ratio’s for different fertilization rates with the fertilizers available to farmers in West-Shewa and Jimma (two major maize belts in Ethiopia). The trials were conducted with additional funding from the TAMASA project and in collaboration with EIAR. As the latter institute is involved in conducting fertilizer trials and the development of recommendations, this collaboration also aimed at forming an appropriate entry point for institutionalization of the decision support tool that is being developed

Maize-Nutrient-Manager: A mobile phone application for field-specific, balanced nutrient management advisory

To increase productivity and profitability, while limiting nutrient losses and related GHG-emissions, African smallholders need more tailored fertilizer advice. Yet, such advice critically hinges upon – largely lacking – field-level management data, as management is key to efficient fertilizer use. The Maize- Nutrient-Manager (MNM) mobile phone application enables collection of such data at scale, and directly converts this data into actionable advice for the farmer. Focusing on field-level management data, MNM can identify those management practices that are currently imperative for enhancing smallholder farmers’ efficient use of fertilizers in their locality, thereby increasing productivity while reducing greenhouse gas (GHG) emissions. This document describes the background, design principles and development process of then MNM mobile phone application, as well as its pilot use in advisory practice in the Mbozi and Momba districts of Songwe region, Tanzania

Rebalancing global nitrogen management in response to a fertilizer and food security crisis

As of Jan 18, 2023 this article is listed as a pre-print and as such has not been peer reviewed by the journalVulnerabilities of the global fuel-fertilizer-food nexus have been revealed by a regional geopolitical conflict causing sudden and massive supply disruptions. Across over- and under-fertilized agricultural systems, nitrogen (N) fertilizer price spikes will have very different effects and require differentiated responses. For staple cereal production in India, Ethiopia, and Malawi, our estimates of N-fertilizer savings show the value of integrated organic and inorganic N management. N-deficient systems benefit from shifting to more cost-effective, high-N fertilizer (such as urea), combined with compost and legumes. N-surplus systems achieve N savings through better targeted and more efficient N-fertilizer use. Globally, there is a need to re-balance access to N-fertilizers, while steering the right fertilizer to the right place, and managing N in combination with carbon through near-term interventions, while striving for longer-term sustainable management. Nationally, governments can invest in extension and re-align subsidies to enable and incentivize improved N management at the farm level

Spatially differentiated nitrogen supply is key in a global food–fertilizer price crisis

A regional geopolitical conflict and sudden massive supply disruptions have revealed vulnerabilities in our global fuel–fertilizer–food nexus. As nitrogen (N) fertilizer price spikes threaten food security, differentiated responses are required to maintain staple cereal yields across over- and underfertilized agricultural systems. Through integrated management of organic and inorganic N sources in high- to low-input cereal production systems, we estimate potential total N-fertilizer savings of 11% in India, 49% in Ethiopia and 44% in Malawi. Shifting to more cost-effective, high-N fertilizer (such as urea), combined with compost and integration of legumes, can optimize N in N-deficient systems. Better targeted and more efficient N-fertilizer use will benefit systems with surplus N. Geospatially differentiated fertilization strategies should prioritize high-N fertilizer supply to low-yield, N-deficient locations and balanced fertilization of N, P, K and micronutrients in high-yield systems. Nationally, governments can invest in extension and realign subsidies to enable and incentivize improved N management at the farm level

Sustainable intensification of smallholder farming systems in Ethiopia : what roles can scattered trees play?

Scattered trees dominate smallholder agricultural landscapes in Ethiopia, as in large parts of sub-Saharan Africa (SSA). While the integration of scattered trees with crops could provide a viable pathway for sustainable intensification of these farming systems, they also lead to trade- offs. Trade-off minimization and benefit maximization from these trees in the system require the processes that underlie tree-crop interactions to be unravelled. This study explored tree- based pathways for the sustainable intensification (SI) smallholder crop production systems in contrasting agroecologies of Ethiopia. Combination of methodologies from agronomy, socio- economics and conservation sciences were utilized to understand the potential roles of scattered trees in smallholder farming systems. Results indicated that farmers maintained on-farm trees because of their direct timber, fencing, fuelwood, and charcoal production values, regardless of their effect on crop productivity. A trade-off analysis revealed that economic gains from trees were not large enough to compensate for tree-induced crop yield penalties in tree-crop mixed farming systems. Under farmers’ practices, most scattered trees generally had a significant negative effect on maize yield. For example, mean maize grain yields were 59%, 42% and 26% less under the canopies of Cordia africana, Croton macrostachyus and Acacia tortilis, respectively, compared with corresponding open field yields. The yield reductions dropped to as low as 5% under ‘good agronomic practices’, such as early planting, variety selection, improved weed management, fine seedbed preparation and higher rates of nitrogen fertilizer. Similar yield reduction was observed in maize under the canopy of Grevillea robusta. Application of nitrogen and phosphorus fertilizers to under canopy maize in Grevillea robusta and Acacia tortilis improved crop yields, compared with non-fertilized maize under the canopies of these tree species. However, recommended rates of nitrogen and phosphorus fertilizers produced significantly less maize yields compared with the open fields. Faidherbia albida is an exceptional scattered tree species that improved soil water, nitrogen and phosphorus use efficiencies, leading to significantly higher yields in wheat gunder tree crown. Available N was 35-55% larger close to the crowns of Faidherbia compared with open fields, apparently contributing as much as 64 kg ha-1 yr-1 mineral N. In addition, this tree significantly reduced photosynthetically active radiation (PAR), reaching the canopy to optimum levels for wheat growth and development. Under the crowns, midday temperature was about 6oC less compared with nearby open fields. Regardless of the triple-win effects (crop production, adaptation and mitigation) of this tree species, over-utilization caused tree population decline. Under the current management, Faidherbia population would decline to a critical density of less than one tree ha-1 within six decades. The current study underlined that conservation of scattered trees can never be achieved through promotions based on neither the trade-offs nor crop productivity benefits involved. Scattered trees can be maintained even when trade-offs with crop production are overriding. Contrarily, these trees may be endangered even if they provide all-round benefits. Thus, a ‘whole sale’ approach that advocates scattered trees on their theoretical environmental and crop production values could jeopardize both conservation and crop production goals. A ‘process-based’ rather than ‘technology-based’ recommendation is required to harness the promising potential that scattered trees offer as a starting point for sustainable intensification of smallholder farming systems

Enhancing Smallholder Wheat Yield Prediction through Sensor Fusion and Phenology with Machine Learning and Deep Learning Methods

Field-scale prediction methods that use remote sensing are significant in many global projects; however, the existing methods have several limitations. In particular, the characteristics of smallholder systems pose a unique challenge in the development of reliable prediction methods. Therefore, in this study, a fast and reproducible new approach to wheat prediction is developed by combining predictors derived from optical (Sentinel-2) and radar (Sentinel-1) sensors using a diverse set of machine learning and deep learning methods under a small dataset domain. This study takes place in the wheat belt region of Ethiopia and evaluates forty-two predictors that represent the major vegetation index categories of green, water, chlorophyll, dry biomass, and VH polarization SAR indices. The study also applies field-collected agronomic data from 165 farm fields for training and validation. According to results, compared to other methods, a combined automated machine learning (AutoML) approach with a generalized linear model (GLM) showed higher performance. AutoML, which reduces training time, delivered ten influential parameters. For the combined approach, the mean RMSE of wheat yield was from 0.84 to 0.98 ton/ha using ten predictors from the test dataset, achieving a 99% confidence interval. It also showed a correlation coefficient as high as 0.69 between the estimated yield and measured yield, and it was less sensitive to the small datasets used for model training and validation. A deep neural network with three hidden layers using the ten influential parameters was the second model. For this model, the mean RMSE of wheat yield was between 1.31 and 1.36 ton/ha on the test dataset, achieving a 99% confidence interval. This model used 55 neurons with respective values of 0.1, 0.5, and 1 × 10−4 for the hidden dropout ratio, input dropout ratio, and l2 regularization. The approaches implemented in this study are fast and reproducible and beneficial to predict yield at scale. These approaches could be adapted to predict grain yields of other cereal crops grown under smallholder systems in similar global production systems

Climate-smart agroforestry: Faidherbia albida trees buffer wheat against climatic extremes in the Central Rift Valley of Ethiopia

Faidherbia albida parklands cover a large area of the Sudano-Sahelian zone of Africa, a region that suffers from soil fertility decline, food insecurity and climate change. The parklands deliver multiple benefits, including fuelwood, soil nutrient replenishment, moisture conservation, and improved crop yield underneath the canopy. Its microclimate modification may provide an affordable climate adaptation strategy which needs to be explored. We carried out an on-farm experiment for three consecutive seasons in the Ethiopian Central Rift Valley with treatments of Faidherbia trees with bare soil underneath, wheat grown beneath Faidherbia and wheat grown in open fields. We tested the sensitivity of wheat yield to tree-mediated variables of photosynthetically active radiation (PAR), air temperature and soil nitrogen, using APSIM-wheat model. Results showed that soil moisture in the sub-soil was the least for wheat with tree, intermediate for sole tree and the highest for open field. Presence of trees resulted in 35–55% larger available N close to tree crowns compared with sole wheat. Trees significantly reduced PAR reaching the canopy of wheat growing underneath to optimum levels. Midday air temperature was about 6 °C less under the trees than in the open fields. LAI, number of grains spike−1, plant height, total aboveground biomass and wheat grain yield were all significantly higher (P < 0.001) for wheat associated with F. albida compared with sole wheat. Model-based sensitivity analysis showed that under moderate to high rates of N, wheat yield responded positively to a decrease in temperature caused by F. albida shade. Thus, F. albida trees increase soil mineral N, wheat water use efficiency and reduce heat stress, increasing yield significantly. With heat and moisture stress likely to be more prevalent in the face of climate change, F. albida, with its impact on microclimate modification, maybe a starting point to design more resilient and climate-smart farming systems

Yield Response and Nutrient Use Efficiencies under Different Fertilizer Applications in Maize (Zea mays L.) In Contrasting Agro Ecosystems

Variability in crop response and nutrient use efficiencies to fertilizer application is quite common under varying soil and climatic conditions. Understanding such variability is vital to develop farm- and area- specific soil nutrient management and fertilizer recommendations. Hence the objectives of this study were to assess maize grain yield response to nutrient applications for identifying yield-limiting nutrients and to understand the magnitude of nutrient use efficiencies under varying soil and rainfall conditions. A total of 150 on-farm nutrient omission trials (NOTs) were conducted on farmers’ field in high rainfall and moisture stress areas. The treatments were control, PK, NK, NP, NPK and NPK+ secondary and micronutrients. Maize grain yield, nutrient uptake, agronomic and recovery efficiencies of N and P differed between fertilizer treatments and between the contrasting agro-ecologies. The AEN ranged from 24.8 to 32.5 kg grain kg-1 N in Jimma area and from 1.0 kg grain kg-1 N (NK treatment) to 10.2 kg grain kg-1 N (NPK treatment) at Adami Tullu and from 0.1 kg grain kg-1 N (NK treatment) to 8.3 kg grain kg-1 N (NPK treatment) at Bulbula. The differing parameters between the agro-ecologies were related to difference in rainfall amount and not to soil factors. Grain yield response to N application and agronomic efficiencies of N and P were higher in the high rainfall area than in the moisture stress areas. Grain yield responded the most to nitrogen (N) application than to any other nutrients at most of the experimental sites. Owing to the magnificent yield response to N fertilizer in the current study, proper management of nitrogen is very essential for intensification of maize productivity in most maize growing areas of Ethiopia

Crop vs. tree : Can agronomic management reduce trade-offs in tree-crop interactions?

Scattered trees dominate smallholder agricultural landscapes in Ethiopia, as in large parts of sub-Saharan Africa (SSA). While the inclusion of scattered trees could provide a viable pathway for sustainable intensification of these farming systems, they also lead to trade-offs. We carried out a study to: 1) explore the rationale of farmers to maintain on-farm trees beyond crop yield; 2) quantify the impact of agronomic practices on the outcome of tree-crop interactions; and 3) analyse partial economic trade-offs for selected on-farm tree species at farm scale. We recorded agronomic practices within the fields of 135 randomly selected farms from seedbed preparation to harvesting. A multivariate analysis showed that farmers maintained on-farm trees because of their direct timber, fencing, fuelwood, and charcoal production values. Trees generally had a significant negative effect on maize yield. Mean grain yields of 1683, 1994 and 1752 kg ha−1 under the canopies of Cordia, Croton and Acacia, respectively, were significantly lower than in their paired open field with mean yields of 4063, 3415 and 2418 kg ha−1. Besides, more income from trees was accompanied by less income from maize, highlighting trade-offs. However, agronomic practices such as early planting, variety used, improved weed management, fine seedbed preparation and higher rates of nitrogen fertilizer significantly reduced yield penalties associated with trees. We found an inverse relationship between land size and on-farm tree density, implying that the importance of trees increases for land-constrained farms. Given the expected decline in per capita land size, scattered trees will likely remain an integral part of these systems. Thus, utilizing ‘good agronomic practices’ will be vital to minimize tree-crop trade-offs in the future

Field Data Collection Methods Strongly Affect Satellite-Based Crop Yield Estimation

Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available